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arXiv 2510.06321quant-phcs.CC
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随机几何二局域哈密顿量动力学中的量子优势

Quantum advantage from random geometrically-two-local Hamiltonian dynamics

Yihui Quek

AI总结:

该研究证明随机几何二局域哈密顿量在常数时间演化下产生经典难解的采样问题,提出首个最坏到平均情况归约,并给出实验可行的量子优势方案。

摘要:

经典采样困难性结果主要针对随机量子电路建立,而模拟模拟器天然实现几何局域哈密顿量下的时间演化。一个典型的此类哈密顿量是否已经产生经典难解的动力学?我们对于具有高斯系数的几何二局域哈密顿量系综,在常数时间演化下,对此问题给出肯定回答。这自然导致一个具有明确实验实现前景的量子优势方案,仅需粗粒度控制。\n我们为这一物理相关系综的困难性提供了强有力的证据。我们发展了首个用于近似(含时无关)几何二局域哈密顿量演化输出概率的最坏情况到平均情况归约。我们的归约采用非标准手段:虽然我们也利用多项式插值,但与先前工作不同,我们直接归约到哈密顿量精确分布的求值器,而我们正试图证明从该分布采样是困难的。先前工作则从真实分布的各种扰动中采样,引入额外约束以控制以全变差距离度量的扰动。我们省去了这一步骤。\n我们的归约由稳健多元多项式插值组成,通过高斯的对称性简化为顺序稳健单变量插值。我们规避了随机哈密顿量缺乏隐藏对称性这一先前证明中的关键性质。我们还贡献了Berlekamp-Welch的算法版本以处理带误差的求值,解决了随机电路采样文献中的一个开放问题。我们期望所发展的机制能在平均情况哈密顿量复杂性中找到应用,填补该文献中迄今集中于最坏情况困难性结果的空白。

英文摘要:

Classical hardness-of-sampling results are largely established for random quantum circuits, whereas analog simulators natively realize time evolutions under geometrically local Hamiltonians. Does a typical such Hamiltonian already yield classically-intractable dynamics? We answer this question in the affirmative for the ensemble of geometrically-2-local Hamiltonians with Gaussian coefficients, evolved for constant time. This naturally leads to a quantum advantage scheme with clear prospects for experimental realization, necessitating only course-grained control. We give strong evidence of hardness for this physically-relevant ensemble. We develop the first worst-to-average-case reduction for approximating output probabilities of (time-independent) geometrically-2-local Hamiltonian evolutions. Our reduction proceeds by nonstandard means: while we also leverage polynomial interpolation, unlike previous works, we reduce directly to an evaluator for the exact distribution over Hamiltonians, from which we are trying to prove that sampling is hard. Previous works instead sampled from various perturbations of the true distribution, introducing additional constraints meant to keep the perturbation, measured in total variation distance, under control. We dispense with this step. Our reduction consists in a robust multivariate polynomial interpolation, reduced to sequential robust univariate interpolations via the symmetries of the Gaussian. We circumvent the fact that random Hamiltonians lack a hiding symmetry, a key property in previous proofs. We also contribute an algorithmic version of Berlekamp-Welch to deal with errored evaluations, solving an open problem from the RCS literature. We expect the machinery we develop to find use in average-case Hamiltonian complexity, filling in a gap in this literature which has thus far focussed on worst-case hardness results.

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